Title: The explanation dialogues: an expert focus study to understand requirements towards explanations within the GDPR Show Chinese title

Title: 解释对话：一项专家焦点研究，以了解GDPR中对解释的要求

Abstract: Explainable AI (XAI) provides methods to understand non-interpretable machine learning models. However, we have little knowledge about what legal experts expect from these explanations, including their legal compliance with, and value against European Union legislation. To close this gap, we present the Explanation Dialogues, an expert focus study to uncover the expectations, reasoning, and understanding of legal experts and practitioners towards XAI, with a specific focus on the European General Data Protection Regulation. The study consists of an online questionnaire and follow-up interviews, and is centered around a use-case in the credit domain. We extract both a set of hierarchical and interconnected codes using grounded theory, and present the standpoints of the participating experts towards XAI. We find that the presented explanations are hard to understand and lack information, and discuss issues that can arise from the different interests of the data controller and subject. Finally, we present a set of recommendations for developers of XAI methods, and indications of legal areas of discussion. Among others, recommendations address the presentation, choice, and content of an explanation, technical risks as well as the end-user, while we provide legal pointers to the contestability of explanations, transparency thresholds, intellectual property rights as well as the relationship between involved parties. Show Chinese abstract

Abstract: 可解释的人工智能（XAI）提供了理解不可解释的机器学习模型的方法。然而，我们对法律专家对这些解释的期望了解甚少，包括它们在法律合规性方面以及与欧盟立法的价值关系。为了填补这一空白，我们提出了解释对话，这是一项专家焦点研究，旨在揭示法律专家和从业者对XAI的期望、推理和理解，特别关注欧洲通用数据保护条例。该研究包括在线问卷和后续访谈，围绕信用领域的使用案例展开。我们使用扎根理论提取了一组层次化且相互关联的代码，并展示了参与专家对XAI的立场。我们发现，所呈现的解释难以理解且信息不足，并讨论了数据控制者和数据主体不同利益可能导致的问题。最后，我们为XAI方法的开发者提出了一些建议，并指出了法律讨论的领域。其中，建议涉及解释的呈现、选择和内容、技术风险以及最终用户，同时我们提供了关于解释可争议性、透明度阈值、知识产权以及相关方之间关系的法律指引。